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<span id="sphx-glr-auto-examples-model-selection-plot-randomized-search-py"></span><h1>Comparing randomized search and grid search for hyperparameter estimation<a class="headerlink" href="#comparing-randomized-search-and-grid-search-for-hyperparameter-estimation" title="Permalink to this headline">¶</a></h1>
<p>Compare randomized search and grid search for optimizing hyperparameters of a
random forest.
All parameters that influence the learning are searched simultaneously
(except for the number of estimators, which poses a time / quality tradeoff).</p>
<p>The randomized search and the grid search explore exactly the same space of
parameters. The result in parameter settings is quite similar, while the run
time for randomized search is drastically lower.</p>
<p>The performance is may slightly worse for the randomized search, and is likely
due to a noise effect and would not carry over to a held-out test set.</p>
<p>Note that in practice, one would not search over this many different parameters
simultaneously using grid search, but pick only the ones deemed most important.</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="nb">print</span><span class="p">(</span><span class="vm">__doc__</span><span class="p">)</span>

<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>

<span class="kn">from</span> <span class="nn">time</span> <span class="kn">import</span> <span class="n">time</span>
<span class="kn">import</span> <span class="nn">scipy.stats</span> <span class="k">as</span> <span class="nn">stats</span>
<span class="kn">from</span> <span class="nn">sklearn.utils.fixes</span> <span class="kn">import</span> <span class="n">loguniform</span>

<span class="kn">from</span> <span class="nn">sklearn.model_selection</span> <span class="kn">import</span> <span class="n">GridSearchCV</span><span class="p">,</span> <span class="n">RandomizedSearchCV</span>
<span class="kn">from</span> <span class="nn">sklearn.datasets</span> <span class="kn">import</span> <span class="n">load_digits</span>
<span class="kn">from</span> <span class="nn">sklearn.linear_model</span> <span class="kn">import</span> <span class="n">SGDClassifier</span>

<span class="c1"># get some data</span>
<span class="n">X</span><span class="p">,</span> <span class="n">y</span> <span class="o">=</span> <span class="n">load_digits</span><span class="p">(</span><span class="n">return_X_y</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>

<span class="c1"># build a classifier</span>
<span class="n">clf</span> <span class="o">=</span> <span class="n">SGDClassifier</span><span class="p">(</span><span class="n">loss</span><span class="o">=</span><span class="s1">&#39;hinge&#39;</span><span class="p">,</span> <span class="n">penalty</span><span class="o">=</span><span class="s1">&#39;elasticnet&#39;</span><span class="p">,</span>
                    <span class="n">fit_intercept</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>


<span class="c1"># Utility function to report best scores</span>
<span class="k">def</span> <span class="nf">report</span><span class="p">(</span><span class="n">results</span><span class="p">,</span> <span class="n">n_top</span><span class="o">=</span><span class="mi">3</span><span class="p">):</span>
    <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="n">n_top</span> <span class="o">+</span> <span class="mi">1</span><span class="p">):</span>
        <span class="n">candidates</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">flatnonzero</span><span class="p">(</span><span class="n">results</span><span class="p">[</span><span class="s1">&#39;rank_test_score&#39;</span><span class="p">]</span> <span class="o">==</span> <span class="n">i</span><span class="p">)</span>
        <span class="k">for</span> <span class="n">candidate</span> <span class="ow">in</span> <span class="n">candidates</span><span class="p">:</span>
            <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Model with rank: </span><span class="si">{0}</span><span class="s2">&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">i</span><span class="p">))</span>
            <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Mean validation score: </span><span class="si">{0:.3f}</span><span class="s2"> (std: </span><span class="si">{1:.3f}</span><span class="s2">)&quot;</span>
                  <span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">results</span><span class="p">[</span><span class="s1">&#39;mean_test_score&#39;</span><span class="p">][</span><span class="n">candidate</span><span class="p">],</span>
                          <span class="n">results</span><span class="p">[</span><span class="s1">&#39;std_test_score&#39;</span><span class="p">][</span><span class="n">candidate</span><span class="p">]))</span>
            <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Parameters: </span><span class="si">{0}</span><span class="s2">&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">results</span><span class="p">[</span><span class="s1">&#39;params&#39;</span><span class="p">][</span><span class="n">candidate</span><span class="p">]))</span>
            <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;&quot;</span><span class="p">)</span>


<span class="c1"># specify parameters and distributions to sample from</span>
<span class="n">param_dist</span> <span class="o">=</span> <span class="p">{</span><span class="s1">&#39;average&#39;</span><span class="p">:</span> <span class="p">[</span><span class="kc">True</span><span class="p">,</span> <span class="kc">False</span><span class="p">],</span>
              <span class="s1">&#39;l1_ratio&#39;</span><span class="p">:</span> <span class="n">stats</span><span class="o">.</span><span class="n">uniform</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">),</span>
              <span class="s1">&#39;alpha&#39;</span><span class="p">:</span> <span class="n">loguniform</span><span class="p">(</span><span class="mf">1e-4</span><span class="p">,</span> <span class="mf">1e0</span><span class="p">)}</span>

<span class="c1"># run randomized search</span>
<span class="n">n_iter_search</span> <span class="o">=</span> <span class="mi">20</span>
<span class="n">random_search</span> <span class="o">=</span> <span class="n">RandomizedSearchCV</span><span class="p">(</span><span class="n">clf</span><span class="p">,</span> <span class="n">param_distributions</span><span class="o">=</span><span class="n">param_dist</span><span class="p">,</span>
                                   <span class="n">n_iter</span><span class="o">=</span><span class="n">n_iter_search</span><span class="p">)</span>

<span class="n">start</span> <span class="o">=</span> <span class="n">time</span><span class="p">()</span>
<span class="n">random_search</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;RandomizedSearchCV took </span><span class="si">%.2f</span><span class="s2"> seconds for </span><span class="si">%d</span><span class="s2"> candidates&quot;</span>
      <span class="s2">&quot; parameter settings.&quot;</span> <span class="o">%</span> <span class="p">((</span><span class="n">time</span><span class="p">()</span> <span class="o">-</span> <span class="n">start</span><span class="p">),</span> <span class="n">n_iter_search</span><span class="p">))</span>
<span class="n">report</span><span class="p">(</span><span class="n">random_search</span><span class="o">.</span><span class="n">cv_results_</span><span class="p">)</span>

<span class="c1"># use a full grid over all parameters</span>
<span class="n">param_grid</span> <span class="o">=</span> <span class="p">{</span><span class="s1">&#39;average&#39;</span><span class="p">:</span> <span class="p">[</span><span class="kc">True</span><span class="p">,</span> <span class="kc">False</span><span class="p">],</span>
              <span class="s1">&#39;l1_ratio&#39;</span><span class="p">:</span> <span class="n">np</span><span class="o">.</span><span class="n">linspace</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="n">num</span><span class="o">=</span><span class="mi">10</span><span class="p">),</span>
              <span class="s1">&#39;alpha&#39;</span><span class="p">:</span> <span class="n">np</span><span class="o">.</span><span class="n">power</span><span class="p">(</span><span class="mi">10</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="o">-</span><span class="mi">4</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="nb">float</span><span class="p">))}</span>

<span class="c1"># run grid search</span>
<span class="n">grid_search</span> <span class="o">=</span> <span class="n">GridSearchCV</span><span class="p">(</span><span class="n">clf</span><span class="p">,</span> <span class="n">param_grid</span><span class="o">=</span><span class="n">param_grid</span><span class="p">)</span>
<span class="n">start</span> <span class="o">=</span> <span class="n">time</span><span class="p">()</span>
<span class="n">grid_search</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">)</span>

<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;GridSearchCV took </span><span class="si">%.2f</span><span class="s2"> seconds for </span><span class="si">%d</span><span class="s2"> candidate parameter settings.&quot;</span>
      <span class="o">%</span> <span class="p">(</span><span class="n">time</span><span class="p">()</span> <span class="o">-</span> <span class="n">start</span><span class="p">,</span> <span class="nb">len</span><span class="p">(</span><span class="n">grid_search</span><span class="o">.</span><span class="n">cv_results_</span><span class="p">[</span><span class="s1">&#39;params&#39;</span><span class="p">])))</span>
<span class="n">report</span><span class="p">(</span><span class="n">grid_search</span><span class="o">.</span><span class="n">cv_results_</span><span class="p">)</span>
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